Rather than designing an optimal and admissible control trajectory, RL [1]} seeks to design a feedback policy to maximize an associated performance index. Unlike the model-based approach described in REF , RL formulations can be model-free, in which an agent interacts directly with the environment, periodically receiving rewards and state observations from the environment. This is particularly valuable for environments that cannot be easily described by differential equations. In this work, we refer to each step of the agent-environment interaction as an action step, \(h\) , and distinguish it from the time step, \(\Delta t\) , associated with the discrete time approximation Eq. (REF ) of the dynamics. It is through this direct interaction with the environment that the agent learns to adjust its policy to maximize the performance index. In this work, we utilize RL to perform trajectory generation for nonlinear aerospace systems subject to complex constraints.
